4.7 Article

Diagnosis of Benign and Malignant Breast Lesions on DCE-MRI by Using Radiomics and Deep Learning With Consideration of Peritumor Tissue

Related references

Note: Only part of the references are listed.
Review Radiology, Nuclear Medicine & Medical Imaging

Role of texture analysis in breast MRI as a cancer biomarker: A review

Rhea D. Chitalia et al.

JOURNAL OF MAGNETIC RESONANCE IMAGING (2019)

Article Health Care Sciences & Services

Utilization of breast cancer screening with magnetic resonance imaging in community practice

Deirdre A. Hill et al.

JOURNAL OF GENERAL INTERNAL MEDICINE (2018)

Review Radiology, Nuclear Medicine & Medical Imaging

Multiparametric MRI of the breast: A review

Maria Adele Marino et al.

JOURNAL OF MAGNETIC RESONANCE IMAGING (2018)

Article Radiology, Nuclear Medicine & Medical Imaging

An MRI-based Radiomics Classifier for Preoperative Prediction of Ki-67 Status in Breast Cancer

Cuishan Liang et al.

ACADEMIC RADIOLOGY (2018)

Article Computer Science, Interdisciplinary Applications

Simultaneous detection and classification of breast masses in digital mammograms via a deep learning YOLO-based CAD system

Mohammed A. Al-masni et al.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2018)

Article Computer Science, Interdisciplinary Applications

Deep Convolutional Neural Networks for breast cancer screening

Hiba Chougrad et al.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2018)

Article Radiology, Nuclear Medicine & Medical Imaging

Invasive Breast Cancer: Prognostic Value of Peritumoral Edema Identified at Preoperative MR Imaging

Hyejin Cheon et al.

RADIOLOGY (2018)

Review Health Care Sciences & Services

Medical Image Analysis using Convolutional Neural Networks: A Review

Syed Muhammad Anwar et al.

JOURNAL OF MEDICAL SYSTEMS (2018)

Article Radiology, Nuclear Medicine & Medical Imaging

Use of clinical MRI maximum intensity projections for improved breast lesion classification with deep convolutional neural networks

Natalia Antropova et al.

JOURNAL OF MEDICAL IMAGING (2018)

Review Oncology

Artificial intelligence in radiology

Ahmed Hosny et al.

NATURE REVIEWS CANCER (2018)

Review Oncology

The role of tumor microenvironment in collective tumor cell invasion

Jia-shun Wu et al.

FUTURE ONCOLOGY (2017)

Article Radiology, Nuclear Medicine & Medical Imaging

Deep Learning in Mammography Diagnostic Accuracy of a Multipurpose Image Analysis Software in the Detection of Breast Cancer

Anton S. Becker et al.

INVESTIGATIVE RADIOLOGY (2017)

Article Computer Science, Artificial Intelligence

Large scale deep learning for computer aided detection of mammographic lesions

Thijs Kooi et al.

MEDICAL IMAGE ANALYSIS (2017)

Article Computer Science, Interdisciplinary Applications

Representation learning for mammography mass lesion classification with convolutional neural networks

John Arevalo et al.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2016)

Article Radiology, Nuclear Medicine & Medical Imaging

Improving the Accuracy of Computer-aided Diagnosis for Breast MR Imaging by Differentiating between Mass and Nonmass Lesions

Cristina Gallego-Ortiz et al.

RADIOLOGY (2016)

Article Radiology, Nuclear Medicine & Medical Imaging

Radiomics: Images Are More than Pictures, They Are Data

Robert J. Gillies et al.

RADIOLOGY (2016)

Article Radiology, Nuclear Medicine & Medical Imaging

Computer-aided evaluation as an adjunct to revised BI-RADS Atlas: improvement in positive predictive value at screening breast MRI

Hye Mi Gweon et al.

EUROPEAN RADIOLOGY (2014)

Article Oncology

Radiomics: Extracting more information from medical images using advanced feature analysis

Philippe Lambin et al.

EUROPEAN JOURNAL OF CANCER (2012)

Review Biochemistry & Molecular Biology

The role of the microenvironment in tumor growth and invasion

Yangjin Kim et al.

PROGRESS IN BIOPHYSICS & MOLECULAR BIOLOGY (2011)

Review Oncology

Mammary field cancerization: molecular evidence and clinical importance

Christopher M. Heaphy et al.

BREAST CANCER RESEARCH AND TREATMENT (2009)

Article Radiology, Nuclear Medicine & Medical Imaging

Quantitative Analysis of Lesion Morphology and Texture Features for Diagnostic Prediction in Breast MRI

Ke Nie et al.

ACADEMIC RADIOLOGY (2008)